1. Field of the Invention
The present invention relates to a computer system supporting the creation of a model used for predicting a phenomenon that changes with the passage of time, such as monthly sales in a store.
2. Description of Related Art
Recently, along with the proliferation of a sensor network, it is becoming easy to collect data indicating various industrial phenomena (for example, sales, environment, a machine, a vital phenomenon). Such data can be used as useful information in various spots such as a retail store and a maintenance spot. Then, an attempt has been made so as to apply a statistic model (mathematical expression) to such data, thereby understanding the nature of a phenomenon indicated by the data, and further predicting a future phenomenon and finding a change in characteristics in an early stage.
One example of such attempts is the creation of a model obtained by performing a regression analysis with respect to data indicating a past phenomenon and expressing the phenomenon by a regression equation. The use of the model enables a past phenomenon to be analyzed or a future phenomenon to be predicted. In the regression equation, a phenomenon to be a target is expressed by an objective variable, and a factor influencing the phenomenon is expressed by an explanatory variable. The objective variable is also referred to as a dependent variable, a response variable, an explained variable, or a criterion variable. The explanatory variable is also referred to as an independent variable or a covariate. The following Expression (1) is an example of a regression equation of linear multiple regression. In the following Expression (1), Y is an objective variable, X1 and X2 are explanatory variables, and a, b and c are constants. In particular, b and c are called partial regression coefficients.Y=a+b·X1+c·X2  (1)
As an example, in the case of predicting sales in a store, the objective variable Y, the explanatory variable X1, and the explanatory variable X2 are respectively defined as a predicted value of sales, a numerical value representing an assortment degree of goods, and an average price of goods in the above Expression (1). In this case, the constants “a”, “b”, and “c” can be obtained, using data on past sales, assortments of goods, and average prices in a plurality of stores (for example, a plurality of chain stores). As a result, for example, a store keeper can compare the respective sales contribution degrees of an assortment and a price of goods in accordance with Expression (1), and can also predict a sales from the assortment and the price of goods.
Thus, in the case of creating a regression equation of a model for analyzing or predicting a phenomenon, it is important to determine what is used as an explanatory variable to be a factor for explaining the phenomenon. This is because a fitting degree varies depending upon how to select an explanatory variable. The determination of such an appropriate explanatory variable cannot help depending upon the experiment, hunch, and trial and error of an analyzer at a spot.
In order to obtain an optimum model, a prediction apparatus has been disclosed, which calculates an error between a predicted value and an actually measured value in a predicted model, and updates the predicted model when the error is large (see, for example, JP 9-95917 A). As another example, a method for selecting a predicted model to be provided, using prediction data in the case of applying time-series achievement data to a plurality of predicted models, has been disclosed (see, for example, JP 2001-22729 A).
However, JP 9-95917 A and JP 2001-22729 A disclose a prediction apparatus and method for modifying a predicted model regarding a particular phenomenon, and do not provide a mechanism of accumulating factors of a predicted model to be used in various information processing apparatuses and utilizing the factors for creating or modifying the predicted model in various information processing apparatuses. Furthermore, along with the recent proliferation of a network, it is expected that a system for accumulating and utilizing information on factors of a predicted model will be demanded more in the future.